A physics-informed deep learning framework for modeling of coronary in-stent restenosis.

Machine learning (ML) techniques have shown great potential in cardiovascular surgery, including real-time stenosis recognition, detection of stented coronary anomalies, and prediction of in-stent restenosis (ISR). However, estimating neointima evolution poses challenges for ML models due to limitations in manual measurements, variations in image quality, low data availability, and the difficulty of acquiring biological quantities. An effective in silico model is necessary to accurately capture the mechanisms leading to neointimal hyperplasia. Physics-informed neural networks (PINNs), a novel deep learning (DL) method, have emerged as a promising approach that integrates physical laws and measurements into modeling. PINNs have demonstrated success in solving partial differential equations (PDEs) and have been applied in various biological systems. This paper aims to develop a robust multiphysics surrogate model for ISR estimation using the physics-informed DL approach, incorporating biological constraints and drug elution effects. The model seeks to enhance prediction accuracy, provide insights into disease progression factors, and promote ISR diagnosis and treatment planning. A set of coupled advection-reaction-diffusion type PDEs is constructed to track the evolution of the influential factors associated with ISR, such as platelet-derived growth factor (PDGF), the transforming growth factor- ß (TGF- ß ), the extracellular matrix (ECM), the density of smooth muscle cells (SMC), and the drug concentration. The nature of PINNs allows for the integration of patient-specific data (procedure-related, clinical and genetic, etc.) into the model, improving prediction accuracy and assisting in the optimization of stent implantation parameters to mitigate risks. This research addresses the existing gap in predictive models for ISR using DL and holds the potential to enhance patient outcomes through predictive risk assessment.

Computational modeling of in-stent restenosis: Pharmacokinetic and pharmacodynamic evaluation.

Persistence of the pathology of in-stent restenosis even with the advent of drug-eluting stents warrants the development of highly resolved in silico models. These computational models assist in gaining insights into the transient biochemical and cellular mechanisms involved and thereby optimize the stent implantation parameters. Within this work, an already established fully-coupled Lagrangian finite element framework for modeling the restenotic growth is enhanced with the incorporation of endothelium-mediated effects and pharmacological influences of rapamycin-based drugs embedded in the polymeric layers of the current generation drug-eluting stents. The continuum mechanical description of growth is further justified in the context of thermodynamic consistency. Qualitative inferences are drawn from the model developed herein regarding the efficacy of the level of drug embedment within the struts as well as the release profiles adopted. The framework is then intended to serve as a tool for clinicians to tune the interventional procedures patient-specifically.

Mechanical modeling of the maturation process for tissue-engineered implants: Application to biohybrid heart valves.

The development of tissue-engineered cardiovascular implants can improve the lives of large segments of our society who suffer from cardiovascular diseases. Regenerative tissues are fabricated using a process called tissue maturation. Furthermore, it is highly challenging to produce cardiovascular regenerative implants with sufficient mechanical strength to withstand the loading conditions within the human body. Therefore, biohybrid implants for which the regenerative tissue is reinforced by standard reinforcement material (e.g. textile or 3d printed scaffold) can be an interesting solution. In silico models can significantly contribute to characterizing, designing, and optimizing biohybrid implants. The first step towards this goal is to develop a computational model for the maturation process of tissue-engineered implants. This paper focuses on the mechanical modeling of textile-reinforced tissue-engineered cardiovascular implants. First, an energy-based approach is proposed to compute the collagen evolution during the maturation process. Then, the concept of structural tensors is applied to model the anisotropic behavior of the extracellular matrix and the textile scaffold. Next, the newly developed material model is embedded into a special solid-shell finite element formulation with reduced integration. Finally, our framework is used to compute two structural problems: a pressurized shell construct and a tubular-shaped heart valve. The results show the ability of the model to predict collagen growth in response to the boundary conditions applied during the maturation process. Consequently, the model can predict the implant's mechanical response, such as the deformation and stresses of the implant.

Bio-Inspired Fiber Reinforcement for Aortic Valves: Scaffold Production Process and Characterization.

The application of tissue-engineered heart valves in the high-pressure circulatory system is still challenging. One possible solution is the development of biohybrid scaffolds with textile reinforcement to achieve improved mechanical properties. In this article, we present a manufacturing process of bio-inspired fiber reinforcement for an aortic valve scaffold. The reinforcement structure consists of polyvinylidene difluoride monofilament fibers that are biomimetically arranged by a novel winding process. The fibers were embedded and fixated into electrospun polycarbonate urethane on a cylindrical collector. The scaffold was characterized by biaxial tensile strength, bending stiffness, burst pressure and hemodynamically in a mock circulation system. The produced fiber-reinforced scaffold showed adequate acute mechanical and hemodynamic properties. The transvalvular pressure gradient was 3.02 ± 0.26 mmHg with an effective orifice area of 2.12 ± 0.22 cm2. The valves sustained aortic conditions, fulfilling the ISO-5840 standards. The fiber-reinforced scaffold failed in a circumferential direction at a stress of 461.64 ± 58.87 N/m and a strain of 49.43 ± 7.53%. These values were above the levels of tested native heart valve tissue. Overall, we demonstrated a novel manufacturing approach to develop a fiber-reinforced biomimetic scaffold for aortic heart valve tissue engineering. The characterization showed that this approach is promising for an in situ valve replacement.

In-vivo assessment of vascular injury for the prediction of in-stent restenosis.

BACKGROUND: Despite optimizations of coronary stenting technology, a residual risk of in-stent restenosis (ISR) remains. Vessel wall injury has important impact on the development of ISR. While injury can be assessed in histology, there is no injury score available to be used in clinical practice. METHODS: Seven rats underwent abdominal aorta stent implantation. At 4 weeks after implantation, animals were euthanized, and strut indentation, defined as the impression of the strut into the vessel wall, as well as neointimal growth were assessed. Established histological injury scores were assessed to confirm associations between indentation and vessel wall injury. In addition, stent strut indentation was assessed by optical coherence tomography (OCT) in an exemplary clinical case. RESULTS: Stent strut indentation was associated with vessel wall injury in histology. Furthermore, indentation was positively correlated with neointimal thickness, both in the per-strut analysis (r = 0.5579) and in the per-section analysis (r = 0.8620; both p ≤ 0.001). In a clinical case, indentation quantification in OCT was feasible, enabling assessment of injury in vivo. CONCLUSION: Assessing stent strut indentation enables periprocedural assessment of stent-induced damage in vivo and therefore allows for optimization of stent implantation. The assessment of stent strut indentation might become a valuable tool in clinical practice.

Lifelike Transformative Materials for Biohybrid Implants: Inspired by Nature, Driven by Technology.

Today's living world is enriched with a myriad of natural biological designs, shaped by billions of years of evolution. Unraveling the construction rules of living organisms offers the potential to create new materials and systems for biomedicine. From the close examination of living organisms, several concepts emerge: hierarchy, pattern repetition, adaptation, and irreducible complexity. All these aspects must be tackled to develop transformative materials with lifelike behavior. This perspective article highlights recent progress in the development of transformative biohybrid systems for applications in the fields of tissue regeneration and biomedicine. Advances in computational simulations and data-driven predictions are also discussed. These tools enable the virtual high-throughput screening of implant design and performance before committing to fabrication, thus reducing the development time and cost of biomimetic and biohybrid constructs. The ongoing progress of imaging methods also constitutes an essential part of this matter in order to validate the computation models and enable longitudinal monitoring. Finally, the current challenges of lifelike biohybrid materials, including reproducibility, ethical considerations, and translation, are discussed. Advances in the development of lifelike materials will open new biomedical horizons, where perhaps what is currently envisioned as science fiction will become a science-driven reality in the future.

A multiphysics modeling approach for in-stent restenosis: Theoretical aspects and finite element implementation.

Development of in silico models that capture progression of diseases in soft biological tissues are intrinsic in the validation of the hypothesized cellular and molecular mechanisms involved in the respective pathologies. In addition, they also aid in patient-specific adaptation of interventional procedures. In this regard, a fully-coupled high-fidelity Lagrangian finite element framework is proposed within this work which replicates the pathology of in-stent restenosis observed post stent implantation in a coronary artery. Advection-reaction-diffusion equations are set up to track the concentrations of the platelet-derived growth factor, the transforming growth factor-ß, the extracellular matrix, and the density of the smooth muscle cells. A continuum mechanical description of volumetric growth involved in the restenotic process, coupled to the evolution of the previously defined vessel wall constituents, is presented. Further, the finite element implementation of the model is discussed, and the behavior of the computational model is investigated via suitable numerical examples. Qualitative validation of the computational model is presented by emulating a stented artery. Patient-specific data are intended to be integrated into the model to predict the risk of in-stent restenosis, and thereby assist in the tuning of stent implantation parameters to mitigate the risk.

Mechanical investigations of the peltate leaf of Stephania japonica (Menispermaceae): Experiments and a continuum mechanical material model.

Stephania japonica is a slender climbing plant with peltate, triangular-ovate leaves. Not many research efforts have been devoted to investigate the anatomy and the mechanical properties of this type of leaf shape. In this study, displacement driven tensile tests with three cycles on different displacement levels are performed on petioles, venation and intercostal areas of the Stephania japonica leaves. Furthermore, compression tests in longitudinal direction are performed on petioles. The mechanical experiments are combined with light microscopy and X-ray tomography. The experiments show, that these plant organs and tissues behave in the finite strain range in a viscoelastic manner. Based on the results of the light microscopy and X-ray tomography, the plant tissue can be considered as a matrix material reinforced by fibers. Therefore, a continuum mechanical anisotropic viscoelastic material model at finite deformations is proposed to model such behavior. The anisotropy is specified as the so-called transverse isotropy, where the behavior in the plane perpendicular to the fibers is assumed to be isotropic. The model is obtained by postulating a Helmholtz free energy, which is split additively into an elastic and an inelastic part. Both parts of the energy depend on structural tensors to account for the transversely isotropic material behavior. The evolution equations for the internal variables, e.g. inelastic deformations, are chosen in a physically meaningful way that always fulfills the second law of thermodynamics. The proposed model is calibrated against experimental data, and the material parameters are identified. The model can be used for finite element simulations of this type of leaf shape, which is left open for the future work.

A grain boundary model considering the grain misorientation within a geometrically nonlinear gradient-extended crystal viscoplasticity theory.

The main goal of the current work is to present a grain boundary model based on the mismatch between adjacent grains in a geometrically nonlinear crystal viscoplasticity framework including the effect of the dislocation density tensor. To this end, the geometrically nonlinear crystal viscoplasticity theory by Alipour et al. (Alipour A et al. 2019 Int. J. Plast. 118, 17-35. (doi:10.1016/j.ijplas.2019.01.009)) is extended by a more complex free energy and a geometrical transmissibility parameter is used to evaluate the dislocation transmission at the grain boundaries which includes the orientations of slip directions and slip plane normals. Then, the grain boundary strength is evaluated based on the misorientation between neighbouring grains using the transmissibility parameter. In some examples, the effect of mismatch in adjacent grains on the grain boundary strength, the dislocation transmission at the grain boundaries and the Hall-Petch slope is discussed by a comparison of two-dimensional random-oriented polycrystals and textured polycrystals under shear deformation.

Fluid-structure interaction simulation of artificial textile reinforced aortic heart valve: Validation with an in-vitro test.

Prosthetic heart valves deployed in the left heart (aortic and mitral) are subjected to harsh hemodynamical conditions. Most of the tissue engineered heart valves have been developed for the low pressure pulmonary position because of the difficulties in fabricating a mechanically strong valve, able to withstand the systemic circulation. This necessitates the use of reinforcing scaffolds, resulting in a tissue-engineered textile reinforced tubular aortic heart valve. Therefore, to better design these implants, material behaviour of the composite, valve kinematics and its hemodynamical response need to be evaluated. Experimental assessment can be immensely time consuming and expensive, paving way for numerical studies. In this work, the material properties obtained using the previously proposed multi-scale numerical method for textile composites was evaluated for its accuracy. An in silico immersed boundary (IB) fluid structure interaction (FSI) simulation emulating the in vitro experiment was set-up to evaluate and compare the geometric orifice area and flow rate for one beat cycle. Results from the in silico FSI simulation were found to be in good coherence with the in vitro test during the systolic phase, while mean deviation of approximately 9% was observed during the diastolic phase of a beat cycle. Merits and demerits of the in silico IB-FSI method for the presented case study has been discussed with the advantages outweighing the drawbacks, indicating the potential towards an effective use of this framework in the development and analysis of heart valves.

Age-related differences in task switching and task preparation: Exploring the role of task-set competition.

The present study focused on the role of task preparation in age-related task-switching deficits. In Experiment 1, we assessed the preparatory reduction of alternation costs (i.e., alternating-task conditions vs. single-task conditions) in twenty-two older adults (65-78years) and 22 young adults (20-28years) by varying the response-stimulus interval (RSI) in a task-switching paradigm with a predictable task sequence and univalent stimuli. In Experiment 2, in which new groups of 22 older adults (65-78years) and 22 young adults (18-24years) took part, we replicated Experiment 1 with bivalent stimuli, which were associated with both tasks and thus increased task-set competition. Whereas in Experiment 1, in which we used univalent stimuli, there were no age-related differences in the preparatory reduction of alternation costs, the data showed impaired task preparation in old age with bivalent stimuli in Experiment 2. These data support the notion that task-preparation deficits in old age occur particularly in situations of increased task-set competition.

Numerical and experimental investigation of the structural behavior of a carbon fiber reinforced ankle-foot orthosis.

Ankle-foot orthoses (AFOs) are designed to enhance the gait function of individuals with motor impairments. Recent AFOs are often made of laminated composites due to their high stiffness and low density. Since the performance of AFO is primarily influenced by their structural stiffness, the investigation of the mechanical response is very important for the design. The aim of this paper is to present a three dimensional multi-scale structural analysis methodology to speed up the design process of AFO. The multi-scale modeling procedure was applied such that the intrinsic micro-structure of the fiber reinforced laminates could be taken into account. In particular, representative volume elements were used on the micro-scale, where fiber and matrix were treated separately, and on the textile scale of the woven structure. For the validation of this methodology, experimental data were generated using digital image correlation (DIC) measurements. Finally, the structural behavior of the whole AFO was predicted numerically for a specific loading scenario and compared with experimental results. It was shown that the proposed numerical multi-scale scheme is well suited for the prediction of the structural behavior of AFOs, validated by the comparison of local strain fields as well as the global force-displacement curves.

Micromechanical modelling of skeletal muscles based on the finite element method.

In the present paper, a new concept for the modelling of skeletal muscles is proposed. An important aspect is the fact that the concept is micromechanically motivated. At the level of the contractile muscle fibres we incorporate the behaviour of the smallest possible unit, the so-called sarcomere, also known as microbiological engine. The contractile fibres (active part of the material) are surrounded by a soft tissue network (passive part of the material). One fundamental advantage of micromechanical approaches in general is the fact that the number of material parameters can be noticeably reduced and the remaining parameters can be usually interpreted physically. The chosen modelling strategy enables the efficient transport of the known information about physiological processes in the fibre to the 3D macroscopic level where, e.g. the dependence of muscle contraction on the stimulus rate is studied. The paper closes with investigations of quasistatic as well as dynamic simulation applied on idealised and non-idealised muscle geometries.